Wasserstein Distributionally Robust Motion Planning and Control with Safety Constraints Using Conditional Value-at-Risk

Wasserstein Distributionally Robust Motion Planning and Control with Safety Constraints Using Conditional Value-at-Risk
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Wasserstein 使用条件风险值进行具有安全约束的分布式鲁棒运动规划和控制

DOI:
10.1109/icra40945.2020.9196857
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发表时间:
2020
期刊:
2020 IEEE International Conference on Robotics and Automation (ICRA)
影响因子:
--
通讯作者:
Insoon Yang
Insoon Yang
中科院分区:
--
文献类型:
--
作者:
A. Hakobyan;Insoon Yang

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在本文中,我们提出了一种基于优化的决策工具,用于在存在随机移动障碍物的环境中进行安全的运动规划和控制。该方法的独特之处在于,即使在瓦瑟斯坦球内障碍物运动的真实概率分布偏离可用的经验分布时,它也可以将不安全风险限制在预先指定的阈值内。另一个优点是它提供了风险约束的概率外性能保证。为了开发一种易于计算的方法来解决分布鲁棒模型预测控制问题,我们提出了一套改写方法,使用(I)Kantorovich对偶原理,(Ii)条件风险值的极值表示,和(Iii)到半空间并的距离的几何表达式。通过使用12维四旋翼模型在三维环境中的仿真,验证了这种分布式稳健方法的性能和实用性。
In this paper, we propose an optimization-based decision-making tool for safe motion planning and control in an environment with randomly moving obstacles. The unique feature of the proposed method is that it limits the risk of unsafety by a pre-specified threshold even when the true probability distribution of the obstacles’ movements deviates, within a Wasserstein ball, from an available empirical distribution. Another advantage is that it provides a probabilistic out-of-sample performance guarantee of the risk constraint. To develop a computationally tractable method for solving the distributionally robust model predictive control problem, we propose a set of reformulation procedures using (i) the Kantorovich duality principle, (ii) the extremal representation of conditional value-at-risk, and (iii) a geometric expression of the distance to the union of halfspaces. The performance and utility of this distributionally robust method are demonstrated through simulations using a 12D quadrotor model in a 3D environment.
DOI: 10.1016/j.orl.2018.01.011
发表时间: 2018-03
期刊: Oper. Res. Lett.
影响因子: --
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通讯作者: Chaoyue Zhao;Yongpei Guan
使用条件风险值的随机系统的安全意识最优控制
DOI: 10.23919/acc.2018.8430957
发表时间: 2018
期刊: 2018 Annual American Control Conference (ACC
影响因子: --
作者:
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通讯作者: Yang, Insoon
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